Overview

Dataset statistics

Number of variables14
Number of observations266
Missing cells844
Missing cells (%)22.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory225.9 B

Variable types

Categorical2
Unsupported2
Numeric10

Alerts

Country Name has a high cardinality: 266 distinct values High cardinality
Country Code has a high cardinality: 266 distinct values High cardinality
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
2000 is highly correlated with 2011 and 8 other fieldsHigh correlation
2011 is highly correlated with 2000 and 8 other fieldsHigh correlation
2012 is highly correlated with 2000 and 8 other fieldsHigh correlation
2013 is highly correlated with 2000 and 8 other fieldsHigh correlation
2014 is highly correlated with 2000 and 8 other fieldsHigh correlation
2015 is highly correlated with 2000 and 8 other fieldsHigh correlation
2016 is highly correlated with 2000 and 8 other fieldsHigh correlation
2017 is highly correlated with 2000 and 8 other fieldsHigh correlation
2018 is highly correlated with 2000 and 8 other fieldsHigh correlation
2019 is highly correlated with 2000 and 8 other fieldsHigh correlation
1990 has 266 (100.0%) missing values Missing
2000 has 34 (12.8%) missing values Missing
2011 has 29 (10.9%) missing values Missing
2012 has 30 (11.3%) missing values Missing
2013 has 31 (11.7%) missing values Missing
2014 has 31 (11.7%) missing values Missing
2015 has 31 (11.7%) missing values Missing
2016 has 32 (12.0%) missing values Missing
2017 has 31 (11.7%) missing values Missing
2018 has 31 (11.7%) missing values Missing
2019 has 32 (12.0%) missing values Missing
2020 has 266 (100.0%) missing values Missing
Country Name is uniformly distributed Uniform
Country Code is uniformly distributed Uniform
Country Name has unique values Unique
Country Code has unique values Unique
1990 is an unsupported type, check if it needs cleaning or further analysis Unsupported
2020 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-04-02 19:57:40.789337
Analysis finished2022-04-02 19:57:51.411770
Duration10.62 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Afghanistan
 
1
St. Lucia
 
1
Serbia
 
1
Seychelles
 
1
Sierra Leone
 
1
Other values (261)
261 

Length

Max length52
Median length9
Mean length12.40225564
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAmerican Samoa
5th rowAndorra

Common Values

ValueCountFrequency (%)
Afghanistan1
 
0.4%
St. Lucia1
 
0.4%
Serbia1
 
0.4%
Seychelles1
 
0.4%
Sierra Leone1
 
0.4%
Singapore1
 
0.4%
Sint Maarten (Dutch part)1
 
0.4%
Slovak Republic1
 
0.4%
Slovenia1
 
0.4%
Solomon Islands1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-02T14:57:51.506514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20
 
4.0%
and12
 
2.4%
income11
 
2.2%
ida10
 
2.0%
africa9
 
1.8%
islands9
 
1.8%
asia8
 
1.6%
ibrd8
 
1.6%
middle7
 
1.4%
rep7
 
1.4%
Other values (310)404
80.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
AFG
 
1
LCA
 
1
SRB
 
1
SYC
 
1
SLE
 
1
Other values (261)
261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowASM
5th rowAND

Common Values

ValueCountFrequency (%)
AFG1
 
0.4%
LCA1
 
0.4%
SRB1
 
0.4%
SYC1
 
0.4%
SLE1
 
0.4%
SGP1
 
0.4%
SXM1
 
0.4%
SVK1
 
0.4%
SVN1
 
0.4%
SLB1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-02T14:57:51.614228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg1
 
0.4%
bhr1
 
0.4%
cpv1
 
0.4%
bdi1
 
0.4%
dza1
 
0.4%
asm1
 
0.4%
and1
 
0.4%
ago1
 
0.4%
atg1
 
0.4%
arg1
 
0.4%
Other values (256)256
96.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1990
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

2000
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct229
Distinct (%)98.7%
Missing34
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean435.9946391
Minimum4.33537483
Maximum4543.436035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:51.725902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.33537483
5-th percentile10.22805648
Q126.78869247
median88.63899237
Q3357.4982757
95-th percentile2216.363526
Maximum4543.436035
Range4539.10066
Interquartile range (IQR)330.7095832

Descriptive statistics

Standard deviation783.7885592
Coefficient of variation (CV)1.797702286
Kurtosis7.499650073
Mean435.9946391
Median Absolute Deviation (MAD)75.56783015
Skewness2.667197511
Sum101150.7563
Variance614324.5056
MonotonicityNot monotonic
2022-04-02T14:57:51.853401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.080703612
 
0.8%
34.219318582
 
0.8%
17.491225792
 
0.8%
49.113048551
 
0.4%
458.55938721
 
0.4%
43.716911321
 
0.4%
1005.2839971
 
0.4%
247.58923341
 
0.4%
203.50627141
 
0.4%
796.64703371
 
0.4%
Other values (219)219
82.3%
(Missing)34
 
12.8%
ValueCountFrequency (%)
4.335374831
0.4%
5.384495261
0.4%
5.971252441
0.4%
7.086915971
0.4%
7.474277971
0.4%
7.544381141
0.4%
8.428345681
0.4%
8.553452491
0.4%
8.560261731
0.4%
9.248229031
0.4%
ValueCountFrequency (%)
4543.4360351
0.4%
4294.7359431
0.4%
3559.8198241
0.4%
2948.9316411
0.4%
2901.1999511
0.4%
2873.8481451
0.4%
2740.6748051
0.4%
2496.0471191
0.4%
2478.0201611
0.4%
2456.4422951
0.4%

2011
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct234
Distinct (%)98.7%
Missing29
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean1027.56258
Minimum12.62923431
Maximum9191.958984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:51.985076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12.62923431
5-th percentile32.77875862
Q185.38583374
median303.9808655
Q3877.1575928
95-th percentile5115.460623
Maximum9191.958984
Range9179.32975
Interquartile range (IQR)791.7717591

Descriptive statistics

Standard deviation1765.835458
Coefficient of variation (CV)1.718469991
Kurtosis6.079448186
Mean1027.56258
Median Absolute Deviation (MAD)251.8833313
Skewness2.521041752
Sum243532.3315
Variance3118174.863
MonotonicityNot monotonic
2022-04-02T14:57:52.115699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255.58865362
 
0.8%
97.752379742
 
0.8%
44.725505642
 
0.8%
50.853473661
 
0.4%
699.96752931
 
0.4%
461.78833011
 
0.4%
872.81146241
 
0.4%
118.97396851
 
0.4%
2909.1176761
 
0.4%
2147.8051761
 
0.4%
Other values (224)224
84.2%
(Missing)29
 
10.9%
ValueCountFrequency (%)
12.629234311
0.4%
15.111170771
0.4%
21.37359811
0.4%
22.845617291
0.4%
23.579858781
0.4%
24.675291061
0.4%
24.93051911
0.4%
25.132408141
0.4%
26.560991291
0.4%
27.067083361
0.4%
ValueCountFrequency (%)
9191.9589841
0.4%
8900.372071
0.4%
8080.2148441
0.4%
7814.1645361
0.4%
7174.7392581
0.4%
6351.2167971
0.4%
6281.0043951
0.4%
5861.7026371
0.4%
5562.0961911
0.4%
5558.2968751
0.4%

2012
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct233
Distinct (%)98.7%
Missing30
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean1017.345743
Minimum14.01603508
Maximum8970.120117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:52.241204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14.01603508
5-th percentile33.11036014
Q189.41673851
median314.7893067
Q3887.8048706
95-th percentile5145.956151
Maximum8970.120117
Range8956.104082
Interquartile range (IQR)798.3881321

Descriptive statistics

Standard deviation1726.638213
Coefficient of variation (CV)1.697199035
Kurtosis6.48397143
Mean1017.345743
Median Absolute Deviation (MAD)256.024185
Skewness2.567237166
Sum240093.5954
Variance2981279.52
MonotonicityNot monotonic
2022-04-02T14:57:52.364900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
264.83361422
 
0.8%
95.747650782
 
0.8%
44.970814172
 
0.8%
51.440261841
 
0.4%
658.12298581
 
0.4%
454.5747071
 
0.4%
829.82159421
 
0.4%
113.44898221
 
0.4%
2591.0161131
 
0.4%
1974.8540041
 
0.4%
Other values (223)223
83.8%
(Missing)30
 
11.3%
ValueCountFrequency (%)
14.016035081
0.4%
19.806379321
0.4%
20.664794921
0.4%
21.517852781
0.4%
22.293680191
0.4%
22.767925261
0.4%
23.459079741
0.4%
24.299861
0.4%
26.230077741
0.4%
27.041063311
0.4%
ValueCountFrequency (%)
8970.1201171
0.4%
8917.1191411
0.4%
8342.5800781
0.4%
8059.5723171
0.4%
6254.4423831
0.4%
6029.4750981
0.4%
6025.3447271
0.4%
6003.8305661
0.4%
5500.6840821
0.4%
5284.6865231
0.4%

2013
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean1058.899939
Minimum16.33388329
Maximum9275.798828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:52.488571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16.33388329
5-th percentile34.61900825
Q1100.6838548
median338.8705026
Q3902.1493038
95-th percentile5180.017384
Maximum9275.798828
Range9259.464945
Interquartile range (IQR)801.465449

Descriptive statistics

Standard deviation1776.595529
Coefficient of variation (CV)1.677774701
Kurtosis6.443545404
Mean1058.899939
Median Absolute Deviation (MAD)279.5032617
Skewness2.551085214
Sum248841.4856
Variance3156291.673
MonotonicityNot monotonic
2022-04-02T14:57:52.611999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
254.33731152
 
0.8%
97.790699662
 
0.8%
51.353249142
 
0.8%
55.034629821
 
0.4%
107.64389041
 
0.4%
877.50299071
 
0.4%
137.64352421
 
0.4%
2629.6054691
 
0.4%
604.76989751
 
0.4%
1370.4676511
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
16.333883291
0.4%
18.830951691
0.4%
19.883651731
0.4%
20.230297091
0.4%
21.938770291
0.4%
24.634164811
0.4%
26.613821031
0.4%
26.673879621
0.4%
27.416616441
0.4%
29.87801171
0.4%
ValueCountFrequency (%)
9275.7988281
0.4%
9241.2656251
0.4%
8522.1259771
0.4%
8214.5506051
0.4%
6696.872071
0.4%
6356.6445311
0.4%
6236.5541991
0.4%
5813.9853521
0.4%
5532.6870121
0.4%
5443.6251
0.4%

2014
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean1081.660092
Minimum19.36799622
Maximum9578.645508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:52.736237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19.36799622
5-th percentile37.20235405
Q195.34088517
median359.3093262
Q3920.9302654
95-th percentile5307.279814
Maximum9578.645508
Range9559.277512
Interquartile range (IQR)825.5893803

Descriptive statistics

Standard deviation1811.702389
Coefficient of variation (CV)1.674927644
Kurtosis6.476116691
Mean1081.660092
Median Absolute Deviation (MAD)291.8125382
Skewness2.549944583
Sum254190.1216
Variance3282265.546
MonotonicityNot monotonic
2022-04-02T14:57:52.859436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253.29663122
 
0.8%
95.13639072
 
0.8%
53.058634142
 
0.8%
59.008934021
 
0.4%
115.32216641
 
0.4%
891.93127441
 
0.4%
137.67100531
 
0.4%
2679.6293951
 
0.4%
549.6961671
 
0.4%
1289.3483891
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
19.367996221
0.4%
20.107210161
0.4%
20.868455891
0.4%
22.271926881
0.4%
23.508329391
0.4%
25.063283921
0.4%
27.04077531
0.4%
29.746252061
0.4%
31.90614511
0.4%
34.447536471
0.4%
ValueCountFrequency (%)
9578.6455081
0.4%
9118.3173831
0.4%
8939.3964841
0.4%
8563.7747031
0.4%
6605.9116211
0.4%
6547.0346681
0.4%
6380.7846681
0.4%
5607.9179691
0.4%
5601.9023441
0.4%
5393.6020511
0.4%

2015
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean989.3898259
Minimum19.00076675
Maximum9392.066406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:52.985095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19.00076675
5-th percentile33.56977234
Q192.90135733
median319.0499268
Q3924.1482544
95-th percentile4628.353076
Maximum9392.066406
Range9373.065639
Interquartile range (IQR)831.2468971

Descriptive statistics

Standard deviation1658.791447
Coefficient of variation (CV)1.676580255
Kurtosis8.365984278
Mean989.3898259
Median Absolute Deviation (MAD)260.1330262
Skewness2.753723483
Sum232506.6091
Variance2751589.064
MonotonicityNot monotonic
2022-04-02T14:57:53.108776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239.6615642
 
0.8%
88.374453862
 
0.8%
55.100075012
 
0.8%
58.906528471
 
0.4%
100.54039761
 
0.4%
958.08489991
 
0.4%
149.96318051
 
0.4%
2349.0544431
 
0.4%
503.75946051
 
0.4%
1108.8781741
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
19.000766751
0.4%
19.643255231
0.4%
19.739574431
0.4%
20.985408781
0.4%
23.232084271
0.4%
23.916238781
0.4%
25.88834191
0.4%
26.820213321
0.4%
30.896455761
0.4%
31.306978231
0.4%
ValueCountFrequency (%)
9392.0664061
0.4%
9382.7910161
0.4%
8915.7976261
0.4%
7565.5498051
0.4%
5598.6816411
0.4%
5469.3349611
0.4%
5421.9321291
0.4%
5148.141291
0.4%
5006.2941151
0.4%
4862.6015631
0.4%

2016
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct231
Distinct (%)98.7%
Missing32
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean1010.63759
Minimum20.57792664
Maximum9775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:53.235431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20.57792664
5-th percentile34.67227764
Q186.78102767
median312.3626552
Q3987.608078
95-th percentile4747.53518
Maximum9775
Range9754.422073
Interquartile range (IQR)900.8270503

Descriptive statistics

Standard deviation1701.37491
Coefficient of variation (CV)1.683466879
Kurtosis8.370189576
Mean1010.63759
Median Absolute Deviation (MAD)255.1862021
Skewness2.752860616
Sum236489.1961
Variance2894676.583
MonotonicityNot monotonic
2022-04-02T14:57:53.366081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
262.18220832
 
0.8%
81.880911842
 
0.8%
56.990216312
 
0.8%
60.188671111
 
0.4%
100.42021941
 
0.4%
1059.457521
 
0.4%
151.47502141
 
0.4%
2376.7211911
 
0.4%
465.08810431
 
0.4%
1175.5970461
 
0.4%
Other values (221)221
83.1%
(Missing)32
 
12.0%
ValueCountFrequency (%)
20.577926641
0.4%
21.783081051
0.4%
21.895393371
0.4%
22.572299961
0.4%
22.653072361
0.4%
25.020866391
0.4%
25.075595861
0.4%
25.439092641
0.4%
29.462802891
0.4%
29.786264421
0.4%
ValueCountFrequency (%)
97751
0.4%
9439.4248051
0.4%
9257.6960391
0.4%
7496.7685551
0.4%
5680.7255861
0.4%
5565.5932621
0.4%
5475.1396481
0.4%
5329.5694011
0.4%
5173.0644691
0.4%
5068.1000981
0.4%

2017
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean1061.555519
Minimum19.41378212
Maximum10103.0918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:53.490907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19.41378212
5-th percentile30.65747719
Q185.45214223
median337.7554016
Q31023.313171
95-th percentile5023.92583
Maximum10103.0918
Range10083.67802
Interquartile range (IQR)937.8610293

Descriptive statistics

Standard deviation1773.830291
Coefficient of variation (CV)1.670972699
Kurtosis7.947057438
Mean1061.555519
Median Absolute Deviation (MAD)279.587738
Skewness2.699889138
Sum249465.5469
Variance3146473.9
MonotonicityNot monotonic
2022-04-02T14:57:53.616569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
263.67640972
 
0.8%
85.452142232
 
0.8%
55.381056822
 
0.8%
65.706024171
 
0.4%
98.582313541
 
0.4%
148.788621
 
0.4%
2526.7204591
 
0.4%
26.381303791
 
0.4%
534.38836671
 
0.4%
1189.0722661
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
19.413782121
0.4%
20.795562741
0.4%
23.155675891
0.4%
24.924812321
0.4%
24.962432861
0.4%
26.381303791
0.4%
28.174047471
0.4%
28.871433261
0.4%
29.768604281
0.4%
30.054405211
0.4%
ValueCountFrequency (%)
10103.09181
0.4%
9606.3496091
0.4%
9573.8071271
0.4%
7815.7866211
0.4%
6144.5463871
0.4%
5837.7705081
0.4%
5800.1528321
0.4%
5713.1186521
0.4%
5515.221741
0.4%
5357.9040671
0.4%

2018
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct232
Distinct (%)98.7%
Missing31
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean1118.583169
Minimum18.52062225
Maximum10515.32324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:53.743230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18.52062225
5-th percentile35.41449228
Q181.16091897
median378.8992468
Q31034.050009
95-th percentile5399.77627
Maximum10515.32324
Range10496.80262
Interquartile range (IQR)952.8890905

Descriptive statistics

Standard deviation1867.295376
Coefficient of variation (CV)1.669339776
Kurtosis7.547247703
Mean1118.583169
Median Absolute Deviation (MAD)317.663426
Skewness2.654844243
Sum262867.0447
Variance3486792.021
MonotonicityNot monotonic
2022-04-02T14:57:53.870892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
273.38364982
 
0.8%
81.160918972
 
0.8%
58.096667532
 
0.8%
69.998603821
 
0.4%
96.38060761
 
0.4%
162.10839841
 
0.4%
2740.2692871
 
0.4%
26.703239441
 
0.4%
564.50329591
 
0.4%
1299.9113771
 
0.4%
Other values (222)222
83.5%
(Missing)31
 
11.7%
ValueCountFrequency (%)
18.520622251
0.4%
22.337013241
0.4%
22.405397421
0.4%
23.979763031
0.4%
24.294088361
0.4%
26.703239441
0.4%
29.698799131
0.4%
30.26614381
0.4%
31.072420121
0.4%
32.05453111
0.4%
ValueCountFrequency (%)
10515.323241
0.4%
9955.4688741
0.4%
9870.6640631
0.4%
8271.8583981
0.4%
6565.8793951
0.4%
6227.0839841
0.4%
6216.7685551
0.4%
6005.0659181
0.4%
5797.7790471
0.4%
5628.7930771
0.4%

2019
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct231
Distinct (%)98.7%
Missing32
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean1125.402047
Minimum19.84997559
Maximum10921.0127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:57:54.000574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19.84997559
5-th percentile33.31404526
Q179.42824762
median384.9846153
Q31059.157379
95-th percentile5427.856665
Maximum10921.0127
Range10901.16272
Interquartile range (IQR)979.7291314

Descriptive statistics

Standard deviation1867.42381
Coefficient of variation (CV)1.659339269
Kurtosis7.984742673
Mean1125.402047
Median Absolute Deviation (MAD)322.9761009
Skewness2.690428406
Sum263344.079
Variance3487271.687
MonotonicityNot monotonic
2022-04-02T14:57:54.127400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.428247622
 
0.8%
60.579452252
 
0.8%
276.55826922
 
0.8%
65.806030271
 
0.4%
111.90448761
 
0.4%
160.69696051
 
0.4%
2711.1928711
 
0.4%
22.638187411
 
0.4%
546.68841551
 
0.4%
1342.0748291
 
0.4%
Other values (221)221
83.1%
(Missing)32
 
12.0%
ValueCountFrequency (%)
19.849975591
0.4%
20.567541121
0.4%
20.570732121
0.4%
22.638187411
0.4%
25.26793481
0.4%
26.742204671
0.4%
29.125291821
0.4%
29.725803381
0.4%
29.850830081
0.4%
30.400287631
0.4%
ValueCountFrequency (%)
10921.01271
0.4%
10317.6831
0.4%
9666.3378911
0.4%
8007.3979491
0.4%
6274.9594731
0.4%
6220.7412111
0.4%
6003.3251951
0.4%
5920.0734011
0.4%
5736.1196741
0.4%
5671.3857421
0.4%

2020
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

Interactions

2022-04-02T14:57:49.417892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:41.013769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.132912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.990539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.847083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.702680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:45.850976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.729792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.628010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.525718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:49.504633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:41.342463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.216716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.072324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.930861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.791443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:45.939715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.816560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.722811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.613510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:49.593419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:41.425257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.301462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.157098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.015605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.879179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.028229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.904326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.811169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.701248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:49.678405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:41.508612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.384239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.240845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.098540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.964974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.116991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.993087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.896940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.790039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:49.767168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:41.594384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.467018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.326479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.182343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:45.050749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.203221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.080853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.982858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.877695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:49.862908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:41.685139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.556834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.412243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.270108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:45.140509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.292007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.171610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.073032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.970448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:50.197987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:41.772902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.639612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.496992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.352858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:45.479603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.376199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.258378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.160392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:49.058240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:50.292762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:41.866625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.727384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.585755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.441386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:45.571426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.462535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.350156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.250424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:49.147973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:50.386483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:41.956411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.813480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.673548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.529639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:45.662774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.551269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.440537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.341240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:49.236735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:50.479254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.045148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:42.902750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:43.761304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:44.614911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:45.757023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:46.640059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:47.535291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:48.430999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:57:49.326138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-02T14:57:54.477322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-02T14:57:54.651823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-02T14:57:54.828380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-02T14:57:54.997925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-02T14:57:50.672777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-02T14:57:50.952082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-02T14:57:51.152436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-02T14:57:51.328991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Country NameCountry Code199020002011201220132014201520162017201820192020
0AfghanistanAFGNaNNaN50.85347451.44026255.03463059.00893458.90652860.18867165.70602469.99860465.806030NaN
1AlbaniaALBNaN80.616951211.075516213.719742236.957962251.358261192.885132202.013321226.280670274.914093NaNNaN
2AlgeriaDZANaN61.582180287.386108335.561951331.941681359.642120291.553528260.761261258.063965256.009460248.205872NaN
3American SamoaASMNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4AndorraANDNaN1302.6207282634.0327152370.0571292432.7827152529.0898442246.2543952361.2175292567.4238282821.8012702744.194336NaN
5AngolaAGONaN12.998967122.107231122.185585143.606873131.647659108.58293995.124977114.33460287.31073871.326004NaN
6Antigua and BarbudaATGNaN512.568298693.807251727.256958711.229797792.841492754.241882778.451111750.019226808.699280760.264954NaN
7ArgentinaARGNaN708.7695311208.3916021368.4229741421.6575931286.2176511531.4842531153.5340581529.6700441127.928467945.991943NaN
8ArmeniaARMNaN26.129089330.575561336.320557396.232544405.728882364.972809357.457764405.650696422.282684523.999084NaN
9ArubaABWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

Country NameCountry Code199020002011201220132014201520162017201820192020
256Post-demographic dividendPSTNaN2478.0201615102.3713115123.8803365156.3071315307.7413095148.1412905329.5694015515.2217405797.7790475920.073401NaN
257Pre-demographic dividendPRENaN17.85619663.03691664.71112371.55132972.88095467.28467663.10217165.44765259.27918259.754262NaN
258Small statesSSTNaN224.064262531.249075536.584470577.526776609.786082579.291462586.392398620.133612643.890895642.824482NaN
259South AsiaSASNaN17.49122644.72550644.97081451.35324953.05863455.10007556.99021655.38105758.09666860.579452NaN
260South Asia (IDA & IBRD)TSANaN17.49122644.72550644.97081451.35324953.05863455.10007556.99021655.38105758.09666860.579452NaN
261Sub-Saharan AfricaSSFNaN34.21931997.75238095.74765197.79070095.13639188.37445481.88091285.45214281.16091979.428248NaN
262Sub-Saharan Africa (excluding high income)SSANaN34.17918297.70876495.69237897.73754195.08027788.31890781.81536285.38805481.09183279.360236NaN
263Sub-Saharan Africa (IDA & IBRD countries)TSSNaN34.21931997.75238095.74765197.79070095.13639188.37445481.88091285.45214281.16091979.428248NaN
264Upper middle incomeUMCNaN109.480538398.925471428.820737465.900197481.174329457.815830449.082922505.204618534.418573554.738718NaN
265WorldWLDNaN478.805717986.353698995.7017411011.8265321034.949121993.9834001015.8749441056.5708621103.3619601121.796355NaN